Shakker AI

Upload the approved 2D character design into the IP-Adapter reference panel to lock the character's visual identity.

Select the OpenPose ControlNet module and upload a sheet of 3D mannequin action poses.

Input a descriptive text prompt detailing the character's lighting and environment, keeping negative prompts strict against anatomical errors.

Generate the image grid, select the most accurate sprites, and use the background removal tool to isolate them for the game engine.

Upload the original product photo and use the auto-mask tool to highlight the plain background.

Engage the Inpainting module and enter a prompt for the new environment, such as 'placed on a sunlit marble kitchen counter, realistic reflections'.

Adjust the denoising strength slider to ensure the AI alters the background completely without modifying the core product pixels.

Generate options, pick the most realistic lighting match, and upscale the final composite for the storefront.

Select a base diffusion model specialized in anime or western comic styles, and stack a specific LoRA to nail the ink weight and color palette.

Use a script or manual prompting to define the scene (e.g., 'extreme close up, character looking surprised, high contrast shading').

Apply character reference images via ControlNet to ensure the protagonist's face remains identical to previous pages.

Generate the panel, apply High-Res fix for sharp linework, and download for final typesetting.

Upload the CAD wireframe or sketch to the ControlNet panel and select the 'MLSD' (Mobile Line Segment Detector) or 'Canny' edge detection model.

Write a detailed architectural prompt specifying materials, time of day, and atmosphere (e.g., 'modern brutalist concrete home, forest environment, twilight, glowing interior lights, photorealistic').

Set the ControlNet weight high enough to strictly follow the structural lines of the sketch.

Generate a batch of 4 variations to present different mood options to the client.

Load the custom-trained LoRA representing the brand's mascot into the prompt formula.

Write prompts placing the mascot in a specific seasonal theme, such as 'wearing a Santa hat, holding a wrapped gift, snowy background'.

Generate a large batch of images to A/B test for the campaign.

Select the best output and use the Outpainting tool to expand the canvas to fit a 16:9 banner aspect ratio, leaving negative space for ad copy.

Enter a base prompt describing the vehicle's function, like 'futuristic hover-tank, heavy armor, cyberpunk aesthetic, concept art'.

Keep the seed randomizer active and set the batch count to generate a grid of 8 low-resolution initial concepts.

Review the grid and find the silhouette or design that sparks the most interest.

Send that specific generation to Image-to-Image, increase the prompt detail, and run an upscaled pass to refine the mechanical greebles.